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Fault detection and identification methodology under an incremental learning framework applied to industrial machinery

Author
Cariño, J. A.; Delgado Prieto, M.; Iglesias, J. A.; Sanchís, A.; Zurita, D.; Millan, M.; Ortega, J.A.; Romero, R.
Type of activity
Journal article
Journal
IEEE access
Date of publication
2018-09-03
Volume
6
First page
49755
Last page
49766
DOI
https://doi.org/10.1109/ACCESS.2018.2868430 Open in new window
Repository
http://hdl.handle.net/2117/127199 Open in new window
URL
https://ieeexplore.ieee.org/document/8454453 Open in new window
Abstract
An industrial machinery condition monitoring methodology based on ensemble novelty detection and evolving classification is proposed in this study. The methodology contributes to solve current challenges dealing with classical electromechanical system monitoring approaches applied in industrial frameworks, that is, the presence of unknown events, the limitation to the nominal healthy condition as starting knowledge, and the incorporation of new patterns to the available knowledge. The proposed m...
Citation
Cariño, J. A., Delgado Prieto, M., Iglesias, J. A., Sanchís, A., Zurita, D., Millan, M., Ortega, J.A., Romero, R. Fault detection and identification methodology under an incremental learning framework applied to industrial machinery. "IEEE access", 3 Setembre 2018, vol. 6, p. 49755-49766.
Keywords
Condition Monitoring, fault diagnosis, industry applications, machine learning
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

Participants

  • Cariño Corrales, Jesús Adolfo  (author)
  • Delgado Prieto, Miquel  (author)
  • Iglesias Martínez, José Antonio  (author)
  • Sanchís, Araceli  (author)
  • Zurita Millán, Daniel  (author)
  • Millan, Marta  (author)
  • Ortega Redondo, Juan Antonio  (author)
  • Romero Troncoso, René  (author)

Attachments